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Contributed Talk Session: Thursday, August 14, 10:00 – 11:00 am, Room C1.04
Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall
Probability Distortions Reflect Boundary Repulsions in Noisy Inference
Saurabh Bedi1, Gilles de Hollander1, Christian C. Ruff1; 1University of Zurich
Presenter: Saurabh Bedi
Probability distortions—the apparent overweighting of small probabilities and underweighting of large ones—is central to decision-making under risk, but its normative and mechanistic origins remain unclear. Traditionally seen as irrational, we propose that probability distortion instead emerges from optimal but noisy inference on bounded quantities. In our proposed account, repulsions arise at natural boundaries of probabilities (0 and 1) due to both resource-rational efficient encoding and Bayesian optimal decoding. Our account predicts that experimental manipulations of boundaries and noise should systematically reshape both probability distortions and behavioral variability, in both risky choice and probability perception. We confirm these predictions in three pre-registered experiments. Our findings reframe probability distortion as a normative consequence of bounded noisy inference and offer a unified mechanistic explanation for its presence across valuation and perception.
Topic Area: Predictive Processing & Cognitive Control
Extended Abstract: Full Text PDF